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How to use execute_actions method in autotest

Best Python code snippet using autotest_python

muti_agent.py

Source:muti_agent.py

...98        time = start99        while time <= end:100            self.EventQueue.put((time, house))101            time += interval102    def execute_actions(self, agent_host, action):103        # agent_host.sendCommand(action)104        # time.sleep(0)105        # world_state = agent_host.getWorldState()106        # for error in world_state.errors:107        #     print("Error:", error.text)108        return109    def face_to(self,agent,face):110        # if self.agent_face[agent]>face:111        #     for i in range(self.agent_face[agent]-face):112        #         self.execute_actions(self.agent_host[agent],"turn -0.8")113        #     time.sleep(0.8)114        #     self.agent_face[agent] = face115        # elif self.agent_face[agent]<face:116        #     for i in range(face-self.agent_face[agent]):117        #         self.execute_actions(self.agent_host[agent],"turn 0.8")118        #     time.sleep(0.8)119        #     self.agent_face[agent] = face120        return121    def move_north(self,agent):122        # self.face_to(agent,1)123        # for i in range(7):124        #     self.execute_actions(self.agent_host[agent],'movenorth 1')125        #     time.sleep(0)126        if agent == 0:127            self.Agent_Location = [(self.Agent_Location[0][0],self.Agent_Location[0][1]-1),self.Agent_Location[1]]128        else:129            self.Agent_Location  = [self.Agent_Location[0], (self.Agent_Location[1][0],self.Agent_Location[1][1]-1)]130    131    def move_south(self,agent):132        # self.face_to(agent,3)133        # for i in range(7):134        #     self.execute_actions(self.agent_host[agent],'movesouth 1')135        #     time.sleep(0)136        if agent == 0:137            self.Agent_Location = [(self.Agent_Location[0][0],self.Agent_Location[0][1]+1),self.Agent_Location[1]]138        else:139            self.Agent_Location  = [self.Agent_Location[0], (self.Agent_Location[1][0],self.Agent_Location[1][1]+1)]140    def move_east(self, agent):141        # self.face_to(agent,2)142        # for i in range(7):143        #     self.execute_actions(self.agent_host[agent],'moveeast 1')144        #     time.sleep(0)145        if agent == 0:146            self.Agent_Location = [(self.Agent_Location[0][0]+1,self.Agent_Location[0][1]),self.Agent_Location[1]]147        else:148            self.Agent_Location  = [self.Agent_Location[0], (self.Agent_Location[1][0]+1,self.Agent_Location[1][1])]149                150    def move_west(self, agent):151        # self.face_to(agent,4)152        # for i in range(7):153        #     self.execute_actions(self.agent_host[agent],'movewest 1')154        #     time.sleep(0)155        if agent == 0:156            self.Agent_Location = [(self.Agent_Location[0][0]-1,self.Agent_Location[0][1]),self.Agent_Location[1]]157        else:158            self.Agent_Location  = [self.Agent_Location[0], (self.Agent_Location[1][0]-1,self.Agent_Location[1][1])]159    def deliever_food(self, agent):160        # if agent == 0:161        #     self.execute_actions(self.agent_host[agent],'moveeast 1')162        #     self.execute_actions(self.agent_host[agent],'moveeast 1')163        #     self.execute_actions(self.agent_host[agent],'moveeast 1')164        #     self.execute_actions(self.agent_host[agent],'movenorth 1')165        #     time.sleep(0)166        #     loc = self.Agent_Location[agent]167        #     self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:air")168        #     self.execute_actions(self.agent_host[agent],'movesouth 1')169        #     self.execute_actions(self.agent_host[agent],'movewest 1')170        #     self.execute_actions(self.agent_host[agent],'movewest 1')171        #     self.execute_actions(self.agent_host[agent],'movewest 1')172        # else:173        #     self.execute_actions(self.agent_host[agent],'moveeast 1')174        #     self.execute_actions(self.agent_host[agent],'moveeast 1')175        #     time.sleep(0)176        #     loc = self.Agent_Location[agent]177        #     self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:air")178        #     self.execute_actions(self.agent_host[agent],'movewest 1')179        #     self.execute_actions(self.agent_host[agent],'movewest 1')180        return 181    def get_path(self, source, dest):182        action = []183        x_axis = dest[0]-source[0]184        y_axis = dest[1]-source[1]185        if x_axis>0:186            for i in range(x_axis):187                action.append('E')188        elif x_axis<0:189            for i in range(-x_axis):190                action.append('W')191        if y_axis>0:192            for i in range(y_axis):193                action.append('S')194        elif y_axis<0:195            for i in range(-y_axis):196                action.append('N')197        return action198        199    def extract_action_list_from_path(self, path_list):200        201        action_trans = {'N':self.move_north,'S':self.move_south,'E':self.move_east,'W':self.move_west}202        alist = []203        for i in path_list:204            alist.append(action_trans[i])205        return alist206    207    def act(self, agent):208        if self.Agent_Action[agent][0] == None:209            self.Agent_Action[agent].pop(0)210            if Tracing:211                print("Stay")212        else:213            self.Agent_Action[agent][0](agent)214            self.Agent_Action[agent].pop(0)215            if Tracing:216                print("agent",agent, "now is on",self.Agent_Location[agent])217        for i in self.House:218            if i.location == self.Agent_Location[agent] and i.state == True:219                if Tracing:220                    print("************Agent", agent, "Change the house", i.id)221                i.state = False222                self.deliever_food(agent)223                total = 0224                for order in i.orderTime:225                    total += self.Clock - order226                i.orderTime = []227                self.total_waiting += (total)228                return 1000 - (total)229        return  0230    def set_toDoAction(self,dest,agent):231        if Tracing:232            print("\t\t\t\tplan to send",agent, "to", dest)233        if dest == self.Agent_Location[agent]:234            self.Agent_Action[agent] = [None]235            if Tracing:236                print("\t\t\t\tWait")237            return238        path = self.get_path(self.Agent_Location[agent], dest)239        extract_action_list = self.extract_action_list_from_path(path)   240        if Tracing:241            print("\t\t\t\t",path,sep="")242            print("\t\t\t\t^^^^^^^^^^^")243        self.Agent_Action[agent] = extract_action_list244        return         245    def get_possible_actions(self,agent):246        return [i.location for i in self.House]247        248    def get_curr_state(self):249        return self.Agent_Location.copy() + [i for i in self.House] 250    def choose_action(self, curr_state, possible_actions, eps,agent):251        if curr_state not in self.q_table[agent]:252            self.q_table[agent][curr_state] = {}253        for action in possible_actions:254            if action not in self.q_table[agent][curr_state]:255                self.q_table[agent][curr_state][action] = 0256        if len(possible_actions) == 0:257            return None258        actionProb = random.uniform(0.0, 1.0)259        # using the random move260        if actionProb <= eps:261            # actionIndex = random.randint(0, len(possible_actions) - 1)262            return possible_actions[random.randint(0, len(possible_actions) - 1)]263        # using greedy action264        else:265            maxQValue = min(self.q_table[agent][curr_state].values()) - 1266            actionList = []267            for (action, qValue) in self.q_table[agent][curr_state].items():268                if qValue > maxQValue:269                    actionList.clear()270                    maxQValue = qValue271                    actionList.append(action)272                elif qValue == maxQValue:273                    actionList.append(action)274            return actionList[random.randint(0, len(actionList) - 1)]275    def transferCurrState(self, state):276        # transfer the state into (num)...277        copy_state = []278        for i in state:279            if type(i) == House:280                if i.state == True:281                    copy_state.append(i.location)282            else:283                copy_state.append(i)284        return tuple(copy_state)285    def best_policy(self):286        returnAction = {"0":[],"1":[]}287        s0 = self.transferCurrState(self.get_curr_state())288        possible_actions1= self.get_possible_actions(0)289        possible_actions2= self.get_possible_actions(1)290        agent1_a0 = self.choose_action(s0, possible_actions1, -1, 0)291        agent2_a0 = self.choose_action(s0, possible_actions2, -1, 1)292        self.set_toDoAction(agent1_a0,0)293        self.set_toDoAction(agent2_a0,1)294        returnAction["0"].append(agent1_a0)295        returnAction["1"].append(agent2_a0)296        done_update = False297        while not done_update:298            count = 0299            if self.EventQueue.qsize() != 0:300                for i in self.EventQueue.queue:301                    if i[0] == self.Clock:302                        count += 1303                for i in range(count):304                    event_time, house = self.EventQueue.get()305                    house.state = True  306                    house.orderTime.append(event_time)307                    loc = house.location 308                    self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:gold_block")309            if Tracing:310                print("----------------------------------------------------")311                print("Now",self.Clock)312                print("Houses:")313                for i in self.House:314                    print("|",i.location,i.state)315            for agent in [0,1]:316                current_r = self.act(agent)317                if self.Agent_Action[agent] == []:318                    if (self.EventQueue.qsize() == 0 and [i.state for i in self.House] == [False for i in self.House]):319                        if agent == 0:320                            returnAction["1"].pop(-1)321                        return returnAction322                    else:323                        s = self.transferCurrState(self.get_curr_state())324                        possible_actions = self.get_possible_actions(agent)325                        next_a = self.choose_action(s, possible_actions, -1,agent)326                        self.set_toDoAction(next_a,agent)327                        returnAction[str(agent)].append(next_a)328            self.Clock += 1329                    330        return returnAction331    def update_q_table(self, tau, S, A, R, T,agent):332        333        # curr_s = S.popleft()334        # curr_a = A.popleft()335        # curr_r = R.popleft()336        # G = sum([self.gamma ** i * R[i] for i in range(len(S))])337        # if tau + self.n < T:338        #     G += self.gamma ** self.n * self.q_table[agent][S[-1]][A[-1]]339        # old_q = self.q_table[agent][curr_s][curr_a]340        # self.q_table[agent][curr_s][curr_a] = old_q + self.alpha * (G - old_q)341        342        curr_s = S.popleft()343        curr_a = A.popleft()344        R.popleft()345        if len(S) != 0:346            nextMaxReward = max(self.q_table[agent][S[0]].values())347        else:348            nextMaxReward = 0349        if len(S) != 0:350            self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (R[0] + self.gamma * nextMaxReward) 351        else:352            self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (self.gamma * nextMaxReward) 353    # def update_q_table(self, tau, S, A, R, T,agent):354        355    #     curr_s = S.popleft()356    #     curr_a = A.popleft()357    #     R.popleft()358    #     nextMaxReward = max(self.q_table[agent][S[0]].values())359    #     self.q_table[agent][curr_s][curr_a] = self.q_table[agent][curr_s][curr_a] + self.alpha * (R[0] + self.gamma * nextMaxReward - self.q_table[agent][curr_s][curr_a]) 360    def run(self):361        S = [deque(), deque()]362        A = [deque(), deque()]363        R = [deque(), deque()]364        present_reward = 0365        done_update = False366        while not done_update:367            s0 = self.transferCurrState(self.get_curr_state())368            possible_actions1= self.get_possible_actions(0)369            possible_actions2= self.get_possible_actions(1)370            agent1_a0 = self.choose_action(s0, possible_actions1, self.epsilon, 0)371            agent2_a0 = self.choose_action(s0, possible_actions2, self.epsilon, 1)372            self.set_toDoAction(agent1_a0,0)373            self.set_toDoAction(agent2_a0,1)374            S[0].append(s0)375            S[1].append(s0)376            A[0].append(agent1_a0)377            A[1].append(agent2_a0)378            R[0].append(0)379            R[1].append(0)380            T = sys.maxsize381            for t in range(sys.maxsize):382                time.sleep(0.1)383                # eventQueue update384                count = 0385                if self.EventQueue.qsize() != 0:386                    for i in self.EventQueue.queue:387                        if i[0] == self.Clock:388                            count += 1389                    for i in range(count):390                        event_time, house = self.EventQueue.get()391                        house.state = True  392                        house.orderTime.append(event_time)393                        loc = house.location 394                        self.execute_actions(self.agent_host[0],"chat /fill "+str(loc[0]*7+2)+" 8 "+str(loc[1]*7+2)+" "+str(loc[0]*7+4)+" 8 "+str(loc[1]*7+3)+" minecraft:gold_block")395                 #-----------------------396                if Tracing:397                    print("----------------------------------------------------")398                    print("Now",self.Clock)399                    print("Houses:")400                    for i in self.House:401                        print("|",i.location,i.state)402                #-----------------------403                if t < T:404                #åä¹åæ¯ä¸æ ·çé»è¾ç»æï¼åªä¸è¿ç°å¨æä¸¤ä¸ªagent405                    for agent in [0,1]:406                        current_r = self.act(agent)407                        if self.Agent_Action[agent] == []:#åªæå½å°è¾¾äºæä¸ä¸ªæ¿åæéè¦è®¡ç® rewardï¼ update q-tableï¼ choose action408                            R[agent].append(current_r)...

evaluate_dagger.py

Source:evaluate_dagger.py

1import copy2import numpy as np3import torch4import os5import sys6sys.path.insert(0, os.environ['ALFWORLD_ROOT'])7from agents.utils.misc import extract_admissible_commands8def evaluate_dagger(env, agent, num_games, debug=False):9    env.seed(42)10    agent.eval()11    episode_no = 012    res_points, res_steps, res_gcs = [], [], []13    res_info = []14    final_dynamics = {}15    with torch.no_grad():16        while(True):17            if episode_no >= num_games:18                break19            obs, infos = env.reset()20            game_names = infos["extra.gamefile"]21            batch_size = len(obs)22            if agent.unstick_by_beam_search:23                smart = [{"not working": [], "to try": []} for _ in range(batch_size)]24            agent.init(batch_size)25            previous_dynamics = None26            execute_actions = []27            prev_step_dones, prev_rewards = [], []28            for _ in range(batch_size):29                execute_actions.append("restart")30                prev_step_dones.append(0.0)31                prev_rewards.append(0.0)32            observation_strings = list(obs)33            task_desc_strings, observation_strings = agent.get_task_and_obs(observation_strings)34            task_desc_strings = agent.preprocess_task(task_desc_strings)35            observation_strings = agent.preprocess_observation(observation_strings)36            first_sight_strings = copy.deepcopy(observation_strings)37            agent.observation_pool.push_first_sight(first_sight_strings)38            if agent.action_space == "exhaustive":39                action_candidate_list = [extract_admissible_commands(intro, obs) for intro, obs in zip(first_sight_strings, observation_strings)]40            else:41                action_candidate_list = list(infos["admissible_commands"])42            action_candidate_list = agent.preprocess_action_candidates(action_candidate_list)43            observation_strings = [item + " [SEP] " + a for item, a in zip(observation_strings, execute_actions)]  # appending the chosen action at previous step into the observation44            still_running_mask = []45            sequence_game_points = []46            goal_condition_points = []47            print_actions = []48            report = agent.report_frequency > 0 and (episode_no % agent.report_frequency <= (episode_no - batch_size) % agent.report_frequency)49            if debug:50                print(first_sight_strings[0])51                print(task_desc_strings[0])52            for step_no in range(agent.max_nb_steps_per_episode):53                # push obs into observation pool54                agent.observation_pool.push_batch(observation_strings)55                # get most recent k observations56                most_recent_observation_strings = agent.observation_pool.get()57                # predict actions58                if agent.action_space == "generation":59                    # heuristically unstick the agent from generating the same thing over and over again60                    prev_actions = copy.copy(execute_actions)61                    if agent.unstick_by_beam_search:62                        for i in range(batch_size):63                            if "Nothing happens" in observation_strings[i]:64                                smart[i]["not working"].append(execute_actions[i])65                    execute_actions, current_dynamics = agent.command_generation_greedy_generation(most_recent_observation_strings, task_desc_strings, previous_dynamics)66                    # heuristically unstick the agent from generating the same thing over and over again67                    if agent.unstick_by_beam_search:68                        for i in range(batch_size):69                            if "Nothing happens" in observation_strings[i] and execute_actions[i] in smart[i]["not working"]:70                                if len(smart[i]["to try"]) == 0:71                                    bs_actions, _ = agent.command_generation_beam_search_generation(most_recent_observation_strings[i: i + 1], task_desc_strings[i: i + 1], None if previous_dynamics is None else previous_dynamics[i: i + 1])72                                    bs_actions = bs_actions[0]73                                    smart[i]["to try"] += bs_actions74                                smart[i]["to try"] = [item for item in smart[i]["to try"] if item != prev_actions[i]]75                                if len(smart[i]["to try"]) > 0:76                                    execute_actions[i] = smart[i]["to try"][0]77                            else:78                                smart[i] = {"not working": [], "to try": []}  # reset79                elif agent.action_space in ["admissible", "exhaustive"]:80                    execute_actions, _, current_dynamics = agent.admissible_commands_greedy_generation(most_recent_observation_strings, task_desc_strings, action_candidate_list, previous_dynamics)81                else:82                    raise NotImplementedError()83                obs, _, dones, infos = env.step(execute_actions)84                scores = [float(item) for item in infos["won"]]85                gcs =[float(item) for item in infos["goal_condition_success_rate"]] if "goal_condition_success_rate" in infos else [0.0]*batch_size86                dones = [float(item) for item in dones]87                if debug:88                    print(execute_actions[0])89                    print(obs[0])90                observation_strings = list(obs)91                observation_strings = agent.preprocess_observation(observation_strings)92                if agent.action_space == "exhaustive":93                    action_candidate_list = [extract_admissible_commands(intro, obs) for intro, obs in zip(first_sight_strings, observation_strings)]94                else:95                    action_candidate_list = list(infos["admissible_commands"])96                action_candidate_list = agent.preprocess_action_candidates(action_candidate_list)97                observation_strings = [item + " [SEP] " + a for item, a in zip(observation_strings, execute_actions)]  # appending the chosen action at previous step into the observation98                previous_dynamics = current_dynamics99                if step_no == agent.max_nb_steps_per_episode - 1:100                    # terminate the game because DQN requires one extra step101                    dones = [1.0 for _ in dones]102                still_running = [1.0 - float(item) for item in prev_step_dones]  # list of float103                prev_step_dones = dones104                step_rewards = [float(curr) - float(prev) for curr, prev in zip(scores, prev_rewards)]  # list of float105                prev_rewards = scores106                sequence_game_points.append(step_rewards)107                goal_condition_points.append(gcs)108                still_running_mask.append(still_running)109                print_actions.append(execute_actions[0] if still_running[0] else "--")110                # if all ended, break111                if np.sum(still_running) == 0:112                    break113            try:114                for n in range(batch_size):115                    Thor = env.envs[n]116                    task_name = Thor.env.save_frames_path117                    final_dynamics[task_name] = {}118                    final_dynamics[task_name]["final_dynamics"] = current_dynamics[n].to('cpu').numpy()119                    final_dynamics[task_name]["label"] = Thor.traj_data['task_type']120            except Exception as e:121                pass122            game_steps = np.sum(np.array(still_running_mask), 0).tolist()  # batch123            game_points = np.max(np.array(sequence_game_points), 0).tolist()  # batch124            game_gcs = np.max(np.array(goal_condition_points), 0).tolist() # batch125            for i in range(batch_size):126                if len(res_points) >= num_games:127                    break128                res_points.append(game_points[i])129                res_gcs.append(game_gcs[i])130                res_steps.append(game_steps[i])131                res_info.append("/".join(game_names[i].split("/")[-3:-1]) + ", score: " + str(game_points[i]) + ", step: " + str(game_steps[i]))132            # finish game133            agent.finish_of_episode(episode_no, batch_size)134            episode_no += batch_size135            if not report:136                continue137            print("Model: {:s} | Episode: {:3d} | {:s} |  game points: {:2.3f} | game goal-condition points: {:2.3f} | game steps: {:2.3f}".format(agent.experiment_tag, episode_no, game_names[0], np.mean(res_points), np.mean(res_gcs), np.mean(res_steps)))138            # print(game_id + ":    " + " | ".join(print_actions))139            print(" | ".join(print_actions))140        average_points, average_gc_points, average_steps = np.mean(res_points), np.mean(res_gcs), np.mean(res_steps)141        print("================================================")142        print("eval game points: " + str(average_points) + ", eval game goal-condition points : " + str(average_gc_points) + ", eval game steps: " + str(average_steps))143        for item in res_info:144            print(item)145        return {146            'average_points': average_points,147            'average_goal_condition_points': average_gc_points,148            'average_steps': average_steps,149            'res_points': res_points,150            'res_gcs': res_gcs,151            'res_steps': res_steps,152            'res_info': res_info...

navigator.py

Source:navigator.py

1from __future__ import annotations2from typing import Optional, Union3from dataclasses import dataclass4from src.svc import common5from src.svc.common import error6from src.svc.common.states import State, SPACE_LITERAL, tree7@dataclass8class Navigator:9    """10    ## Traces user's path between states11    So he can use `â Back` button12    """13    trace: list[State]14    """15    # Example:16    ```17    [ Settings.I_MAIN, Settings.II_GROUP, Settings.I_BROADCAST ]18            ^                             ^19        where started                current state20    ```notpython21    """22    back_trace: list[State]23    """24    # Current state moves here when you press `Back` button25    - so you can use "Next" button26    """27    ignored: set[State]28    """29    # States that user is not supposed to get to30    """31    everything: Optional[common.CommonEverything]32    """33    # Last recieved event34    ## Used35    - to pass it to `on_enter`, `on_exit` methods of states36    """37    @classmethod38    def default(cls: type[Navigator]):39        return cls(40            trace      = [tree.Init.I_MAIN],41            back_trace = [],42            ignored    = set(),43            everything = None44        )45    @property46    def current(self) -> Optional[State]:47        """48        ## Last state from `trace`49        """50        if len(self.trace) < 1:51            return None52        return self.trace[-1]53    @property54    def first(self) -> Optional[State]:55        if len(self.trace) < 1:56            return None57        58        return self.trace[0]59    @property60    def current_back_trace(self) -> Optional[State]:61        """62        ## Last state from `back_trace`63        """64        if len(self.back_trace) < 1:65            return None66        67        return self.back_trace[-1]68    @property69    def space(self) -> SPACE_LITERAL:70        """71        ## Get space of current state72        """73        return self.current.space74    @property75    def previous_space(self) -> SPACE_LITERAL:76        for state in reversed(self.trace):77            if state.space != self.space:78                return state.space79        80        return self.space81    def append(self, state: State):82        """ ## Add state to trace """83        if state in self.ignored:84            raise error.GoingToIgnoredState(85                f"appending {state.anchor} that is ignored: {self.ignored}"86            )87        88        if self.current is state:89            raise error.DuplicateState(90                f"appending {state.anchor} that was the current one"91            )92        93        if self.current_back_trace is state:94            return self.next()95        self.append_no_checks(state)96    def append_no_checks(self, state: State):97        # we exit current state, 98        # but it will remain99        # in trace, we can go back100        # to it, so we call `on_traced_exit`101        if self.current:102            self.current.on_traced_exit(self.everything)103    104        self.trace.append(state)105        # we entered a state106        # for the first time,107        # it wasn't in trace before,108        # so we call `on_enter`109        self.current.on_enter(self.everything)110    def back(111        self, 112        trace_it: bool = True,113        execute_actions: bool = True114    ):115        """116        # Remove last state from current space117        ## Params118        - `trace_it` - if current state119        should be appended to `back_trace`120        - `execute_actions` - we can assign121        actions to do when we enter/exit122        specific state. This parameter123        tells if we should execute them124        """125        if len(self.trace) < 2:126            return None127        if trace_it and self.trace[-1].back_trace:128            self.back_trace.append(self.current)129        if execute_actions:130            # this state won't be in trace131            # anymore, so we call `on_exit`132            self.current.on_exit(self.everything)133        del self.trace[-1]134        if execute_actions:135            # state we just got to was136            # in trace, so we call `on_traced_enter`137            self.current.on_traced_enter(self.everything)138    def next(self):139        if len(self.back_trace) > 0:140            self.append_no_checks(self.current_back_trace)141            del self.back_trace[-1]142    143    def delete(self, state: State) -> bool:144        for (i, traced_state) in enumerate(self.trace):145            if traced_state == state:146                # MOTHERFUCKER GETS EJECTED147                traced_state.on_delete(self.everything)148                del self.trace[i]149                return True150        151        return False152    153    def delete_back_trace(self, state: State) -> bool:154        for (i, traced_state) in enumerate(self.back_trace):155            if traced_state == state:156                # MOTHERFUCKER GETS EJECTED157                traced_state.on_delete(self.everything)158    159                del self.back_trace[i]160                return True161        162        return False163    @property164    def spaces(self) -> set[SPACE_LITERAL]:165        unique_spaces: set[SPACE_LITERAL] = set()166        for state in self.trace:167            unique_spaces.add(state.space)168        169        return unique_spaces170    def is_space_mixed(self) -> bool:        171        return len(self.spaces) > 1172    def jump_back_to(173        self, 174        state: State, 175        trace_it: bool = False, 176        execute_actions: bool = True177    ):178        if state not in self.trace:179            raise error.ThisStateNotInTrace(180                "you tried to jump back to state "181                "that is not in trace"182            )183        184        while self.current != state:185            self.back(186                trace_it = trace_it, 187                execute_actions = execute_actions188            )189    190    def space_jump_back(self, trace_it: bool = False):191        if not self.is_space_mixed():192            raise error.NotSpaceMixed(193                "jumping back from current space to other isn't possible, "194                "'cause there's only one type of space in trace"195            )196        initial_space = self.space197        while initial_space == self.space:198            self.back(trace_it = trace_it)199    200    def jump_back_to_or_append(self, state: State, trace_it: bool = False):201        try:202            self.jump_back_to(state, trace_it = trace_it)203        except error.ThisStateNotInTrace:204            self.append(state)205    206    def clear(self) -> None:207        self.trace = []208        self.back_trace = []209        self.ignored = set()210    211    def auto_ignored(self):212        if tree.Init.I_MAIN in self.spaces:213            self.ignored.add(tree.Settings.I_MAIN)214        215        if tree.Settings.I_MAIN in self.spaces:216            ...217        218        if not self.everything.is_group_chat:...

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